ON THE USE OF MULTI-DIMENSIONAL CHANNEL SOUNDING FIELD MEASUREMENT DATA FOR SYSTEM- LEVEL PERFORMANCE EVALUATIONS

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1 EUROPEAN COOPERATION IN THE FIELD OF SCIENTIFIC AND TECHNICAL RESEARCH COST 273 TD(02) 164 Lisbon, Portugal 2002/Sep/19-20 EURO-COST SOURCE: University of Oulu, Finland ON THE USE OF MULTI-DIMENSIONAL CHANNEL SOUNDING FIELD MEASUREMENT DATA FOR SYSTEM- LEVEL PERFORMANCE EVALUATIONS Tad Matsumoto University of Oulu Oulu Finland

2 ON THE USE OF MULTI- DIMENSIONAL CHANNEL SOUNDING FIELD MEASUREMENT DATA FOR SYSTEM-LEVEL PERFORMANCE EVALUATIONS Tad Matsumoto 1, Uwe Trautwein 2, Christian Schneider 3, Reiner Thomä 3 1 University of Oulu, Oulu, Finland 2 Medav / Tewisoft GmbH, Ilmenau, Germany 3 Technische Universität Ilmenau, Ilmenau, Germany tadashi.matsumoto@ee.oulu.fi Abstract: Current advances in multi-dimensional channel-sounding techniques make it possible to evaluate performances of radio multiple access and signal processing schemes in realistic propagation conditions. This paper focuses on how recorded impulse response data gathered through multidimensional channel sounding field measurements can be used to evaluate system-level performances of the radio access schemes. Methods for link- and system-level simulations are presented. Performance curves of a space-time equalizer, obtained as the results of the simulations, are also presented as an example to demonstrate the effectiveness of the evaluation scheme using the field measurement data. 1 Introduction Evaluating system level performance figures such as outage probability and/or blocking probability is crucial when determining new air interface specifications for future mobile communication systems. The system level performance figures can be estimated from the geographical distributions of linklevel performance figures of the radio access schemes and signal processing algorithms such as bit error rate (BER) and signal to interference plus noise power ratio (SINR). Previously, such system level performances have been evaluated through field experiments using system prototype. The prototyping, however, has imposed heavy burden on those who don t have enough budget or human resource. It is quite natural to set the bit rate targets for post-third generation (3G) systems higher than 3G s maximum speed, with the aim of supporting real-time multimedia communications. Given that the system bandwidth needed to realize broadband communication may not be fully available, the post-3g networks will have to have greater resistance to co-channel interference (CCI). It is obvious that broadband signaling over mobile radio channels imposes severe inter-symbol interference (ISI) upon the received signals due mainly to the effect of multipath propagation. Hence, a technological breakthrough that can reduce the effects of CCI and ISI while taking full advantage of the benefits of multipath transmission is needed. Furthermore, unlike the 3G and prior systems, which mainly exploit the received signal s temporal structure, post-3g systems must jointly exploit the received signal s temporal and spatial structures. Such systems having resistance against CCI and ISI should be rather complex, and hence huge risk has to be tolerated even for its prototyping. A lot of efforts have been made to create a new propagation model 1] that can well express the two dimensional (spatial and temporal) channel structure. The model-based simulation is quite effective in 1

3 evaluating fundamental performances of radio access schemes. However, when performances have to be evaluated in more realistic conditions, field measurements using system prototype is still required. Current advances 2], 3] in multi-dimensional channel-sounding techniques make it possible to bridge the gap between the model-based simulations and filed measurements using system prototype. Multidimensional channel sounder systems have multiple receive antenna elements. Received channel sounding waveform transmitted periodically from the transmitter is analyzed at the receiver, and the channel impulse response (CIR) from the transmitter to each of the antenna elements is recorded. This data is then used for off-line simulations 4], 5]. Since the CIR data represents realistic propagation scenario, results of the off-line simulations accurately express performances of the radio access schemes in real fields. Furthermore, if performances of different radio access and signal processing schemes are evaluated using the same CIR data through off-line simulations, fair comparison can be made among the candidate schemes. This should avoid the risk of prototyping, thereby it is made possible for those who don t have enough budgetary and human resources to make substantial contributions to the post- 3G system concept creation. This paper focuses on how recorded impulse response data gathered through multi-dimensional channel sounding field measurements is used to evaluate system-level performances of the radio access schemes. Methods for link- and system-level simulations are presented. Performance curves of a space-time equalizer, obtained as the results of the simula tions, are presented as an example to demonstrate the effectiveness of the evaluation scheme using the field measurement data. 2 Link- and System-Level Simulations We define two terms used in this article, link-level simulation and system-level simulation. Both are off-line simulations using multidimensional channel-sounding field measurement data, but their goals are different: (1) Link-level simulation aims to evaluate ST-equalizer link-performance in terms of bit error rate (BER) in various propagation environments. It focuses on analyzing/evaluating the impact of ST-equalizer configurations, parameters, and algorithms on performance. (2) System-level simulation aims to obtain system-level performance figures such as signal-to-interference plus noise power ratio (SINR) and BER in terms of either geographical distributions in the area of interest or outage probability. Main focus is on interference scenarios, which depend on cell design including frequency reuse and user distribution. 2.1 Field Measurements Prior to the simulations, multi-dimensional channel sounding field measurements have to be conducted based on the business scenario considered: e.g., indoor or outdoor, micro-cell or macro-cell, frequency band, and/or duplex method. The field measurement campaign then takes place to collect enough sets of CIR data. Figure 1 shows as an example the locations of CIR measurements conducted in downtown Tokyo, where a typical sectored macro-cell scenario was assumed: There are degree sectors, A 1, B 1, B 2, and B 3, in their combined triangle. The business scenario in this example is that each sector has one user using the same frequency and time slots. The antenna array of the channel-sounder was located on top of a building with a height of approximately 40 meters. Other surrounding buildings had almost the same height. The transmitter was moved along a street. There was no line of sight between the transmitter and receiver in all propagation scenarios investigated. The output of the field measurement is a series of data sets indicating the measured impulse responses of the channels between the transmitter s omni-directional antenna and each of the 8 elements of the receiver antenna array. 2

4 2.2 Link-Level Simulation The basic concept of link-level simulation is to replace CIR generated according to a model by the measured CIR data. Since the CIR data between the omni-directional transmitter antenna and each of the L antenna elements is available, each element s received signal waveform can be calculated by convoluting the transmitted desired waveform, corresponding to the radio access scheme to be evaluated, with the measured CIR data. Received waveforms of co-channel interference signal can also be calculated in the same way from the CIR data measured at different transmitter positions. 2.3 System-Level Simulation Assume that the user in A 1 is desired, and those in B 1, B 2, and B 3 are interference users. Also assume that there are i, j, k, and l sets of measured CIR data in the sector A 1, B 1, B 2, and B 3, respectively. Then, there are ijkl combinations of locations. By performing the link-level simulations for each of the ijkl locations, cumulative distribution of the link-level performance figures can be obtained. From the obtained cumulative distribution curve, it can easily be estimated that at what percent of the geographical area, acceptable communication quality can be achieved. Planning of a field measurement campaign should reflect the assumed interference scenario in terms of reuse distance, and cell layout. Obviously, the larger the ijkl value, the better the obtained cumulative distribution curve reflects the real scenario. 3 Example This section describes as an example the results of system-level simulations conducted to evaluate a space-time (S/T-) equalizer 4]. Figure 2 shows a block diagram of the S/T-equalizer. It consists of a cascaded connection of an adaptive array antenna and the maximum likelihood sequence estimator (MLSE) 5], 6]: each of the adaptive array antenna elements is equipped with a fractiona lly spaced tapped delay line (FTDL), and the MLSE has taps covering a portion of the channel delay profile (This S/T-equalizer configuration is referred to as FTDL/MLSE for convenience). The MLSE estimates the sequence considered most likely to have been transmitted. Key parameters are the numbers M, and N of the antenna elements, the FTDL taps, and the taps in MLSE, respectively, which are expressed as ( M, N) for notation convenience. The N taps in MLSE are used to replicate the signal at the array output corresponding to the symbol sequence selected by MLSE. There are LMN taps that have to be adaptively determined according to the channel conditions such as incident angles and strengths of the desired and interference users multipath components. MLSE uses the Viterbi algorithm, for which the number of the states is Q N-1 for Q-level signaling. For quaternary phase shifted keying (QPSK), Q=4. The LMN tap weights are determined so that H W X min, (1) where vectors W and X are the weight and sampled data vectors, respectively. The first LM entries of W are the tap weights on the L antenna elements, and the following N entries are the MLSE taps, as T W = W, W with and a t ] W = W, W, W, W, W, W, W, W, W ] (2) a a11 a12 a1m a21 a22 a2m al1 al2 alm W = W, W, W ]. (3) t t1 t2 tn The data vector X has the same structure as W, as X = X, X, X ] with and t t1 t2 tn X = X, X X, X, X X, X,X X ] (4) a a11 a12 a1m a21 a22 a2m al1 al2 alm 3

5 X = X, X, X ]. (5) t t1 t2 tn Elements of X a are of the samples taken at the outputs of FTDL on the L antenna elements. During the training period, elements of X t are the sequence of signal points corresponding to the signal reference (= unique word sequence) transmitted at the head of the frame. The tap weights can be determined recursively by using the recursive least square (RLS) algorithm. As shown in Fig. 1, four users were considered in the simulations. The user in A 1 is desired, and those in B 1, B 2, and B 3 are interference users. Assuming that the attenuation in signal strength due to the path-loss is proportional to the 3.7th power of distance, and that shadowing is an independent random variable distributed over a Log-Normal distribution with standard deviation of 10.0 db, overall loss with the channel between each user and its home base station was calculated. Pass-losses due to the distances between each interferer s location and desired user s base station were also calculated, and their corresponding shadowing losses were computer-generated, and multiplied by the pass-losses. The desired and interference users were assumed to be power-controlled by their home base stations so that the received signal-to-noise power ratio (SNR) becomes 15 db. The effect of the power control on strengths of the interferers signals received by the desired user s base station was then taken into account. There are 11, 8, 11, and 11 measurement points in A 1, B 1, B 2, and B 3, respectively (=11 x 8 x 11 x 11) combinations were taken by randomly choosing the desired and three interference users locations from among the 41 field measurement points. For each of the combinations, the overall channel losses due to the distance, shadowing, and power-control were calculated, and the channel losses were multiplied by the field measurement CIR data. The signal processing for the S/Tequalizer was then performed, and the cumulative distribution function of the MLSE input SINR defined as Total of Combinable Path Energies SINR = (7) Mean Squared Error after Convergence of the RLS Algorithm was evaluated using the combinations of the field measurement data. Figure 3 shows cumulative distribution functions of the MLSE input SINR for ( M, N)=(8, 7, 5), (8, 1, 1), (1, 7, 5), and (1, 1, 1). Obviously, the performance with the (1, 1, 1) configuration is the worst among them. Systems using the (1, 1, 1) configuration (=non-adaptive omni-directional antenna) may need spreading of the signals in the frequency domain to achieve the process gain at the receiver side. Neither the (1, 7, 5) configuration (=omni-directional antenna with FTDL/MLSE temporal-equalizer) nor the (8, 1, 1) configuration (=8-element adaptive array antenna) achieves reasonable performance if the requirement for the system outage is 5%. Even if the outage requirement is 10%, they need auxiliary technique such as error correcting coding that has to work properly with 1..2 db SINR. The (8, 7, 1) configuration (S/T-equalizer without MLSE) achieves much better performance than the (1, 1, 1), (1, 7, 5), and (8, 1, 1) configurations: if the requirement for the system outage is 5%, the receiver has to work properly with 6 db input SINR. Obviously, the (8, 7, 5) configuration achieves the best performance: the cumulative distribution curve for (8, 7, 5) indicates that at 95% of the sector A 1, the MLSE input SINR can be made larger than or equal to 8 db. In fact, 8 db input SINR is sufficient for MLSE to achieve acceptable level of BER, say, 10-6, that would support multi-media communications, with a help of simple error correction coding. 4 Conclusions The main focus of this paper has been on how recorded impulse response data gathered through multidimensional channel sounding field measurements can be used to evaluate system-level performances of the radio access schemes. Methods for link- and system-level simulations were presented. As an example of the link-level simulation, methodology for evaluating up-link system-level performance of a system utilizing a space-time equalizer is briefly described. 4

6 5 References 1] Correia, L. M. (Ed) Wireless Flexible Personalised Communications: COST 259 European co-operation in Mobile Radio Research, Wiley, ] Elektrobit AG, 3] MEDAV GmbH, 4] R. S. Thomä, D. Hampicke, A. Richter, G. Sommerkorn, U. Trautwein, MIMO Vector Channel Sounder Measurement for Smart Antenna System Evaluation, European Trans. on Telecomm., ETT Vol. 12, No. 5, Sept./Oct ] T. Jämsä, J. Kolu, Tsukamoto, T. Matsumoto, Real-Time Simulation of Adaptive Array Antenna using Broadband Vector Channel Simulator, to be presented on WPMC 2002, Hawai 6] T. Matsumoto, T. Yamada, and S. Tomisato, System-Level Performance Evaluations of Spatio-Temporal Equalizers Using Two-Dimensional Channel Sounding Field Measurement Data, Conf. Record, 27th General Assembly of International Union of Radio Science Maastricht, The Netherlands, 19 August, ] T. Yamada, S. Tomisato, T. Matsumoto, and U. Trautwein, Results of Link-Level Simulations Using Field Measurement Data for an FTDL-Spatial/ MLSE-Temporal Equalizer, IEICE Trans. Commun., Vol. E84-B, No. 7 July 2001, pp ] T. Yamada, S. Tomisato, T. Matsumoto, and U. Trautwein, Performance Evaluation of FTDL-Spatial/MLSE-Temporal Equalizers in the Presence of Co-Channel Interference Link-Level Simulation Results Using Field Measurement Data -, IEICE Trans. Commun., Vol. E84-B, No. 7 July 2001, pp Sector B 1 BS A Sector A 1 Array Receiver Approx. 500m : Measurement Point Sector B 2 Sector B 3 There are: 11 Points in Sector A 1, 8 Points in Sector B 1, 11 Points in Sector B 2, and 11 Points in Sector B (=11 x 8 x 11 x 11) Combinations in Total Fig. 1 Field Measurements for System-Level Simulations 5

7 #1 X a11 X a12 X a1(m-1) X a1m t=nt T/2 T/2 W a11 W a12 W a1(m-1) W a1m - MLSE Viterbi 2 Algorithm #2 X tn X t(n-1) X T t2 X t1 T #L W tn W t(n-1) W t2 W t1 =1 (Const.) Key Parameters ( M, N)are the Numbers of: Antenna Elements Feed-Forward Filter Taps M on Each Antenna Element, MLSE Taps N. There are LMN Taps in this Configuration. Fig. 2 S/T-Equalizer Configuration 100 Cumulative Distribution Function (%) 10 w/o Equalizer (1.1.1) Temporal Equalizer S/T-Equalizer (1,7,5) w/ MLSE (8,7,5) Spatio Equalizer (8,1,1) S/T-Equalizer w/o MLSE (8,7,1) 1 Desired User, 3 Interferers Timing Offset= MLSE Input SINR (db) Fig. 3 Results of System-Level Simulations for a S/T-Equalizer Configuration 6